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pro vyhledávání: '"Sheth, Amit"'
Monitoring public sentiment via social media is potentially helpful during health crises such as the COVID-19 pandemic. However, traditional frequency-based, data-driven neural network-based approaches can miss newly relevant content due to the evolv
Externí odkaz:
http://arxiv.org/abs/2411.07163
In the era of Generative AI, Neurosymbolic AI is emerging as a powerful approach for tasks spanning from perception to cognition. The use of Neurosymbolic AI has been shown to achieve enhanced capabilities, including improved grounding, alignment, ex
Externí odkaz:
http://arxiv.org/abs/2411.03225
Autor:
Suresh, Suryavardan, Rani, Anku, Patwa, Parth, Reganti, Aishwarya, Jain, Vinija, Chadha, Aman, Das, Amitava, Sheth, Amit, Ekbal, Asif
Researchers have found that fake news spreads much times faster than real news. This is a major problem, especially in today's world where social media is the key source of news for many among the younger population. Fact verification, thus, becomes
Externí odkaz:
http://arxiv.org/abs/2410.04236
Attribution in large language models (LLMs) remains a significant challenge, particularly in ensuring the factual accuracy and reliability of the generated outputs. Current methods for citation or attribution, such as those employed by tools like Per
Externí odkaz:
http://arxiv.org/abs/2410.03726
The current method for predicting causal links in knowledge graphs uses weighted causal relations. For a given link between cause-effect entities, the presence of a confounder affects the causal link prediction, which can lead to spurious and inaccur
Externí odkaz:
http://arxiv.org/abs/2410.14680
Causal networks are often incomplete with missing causal links. This is due to various issues, such as missing observation data. Recent approaches to the issue of incomplete causal networks have used knowledge graph link prediction methods to find th
Externí odkaz:
http://arxiv.org/abs/2410.14679
Autor:
Balappanawar, Ishwar B, Chamoli, Ashmit, Wickramarachchi, Ruwan, Mishra, Aditya, Kumaraguru, Ponnurangam, Sheth, Amit P.
Distracted driving is a leading cause of road accidents globally. Identification of distracted driving involves reliably detecting and classifying various forms of driver distraction (e.g., texting, eating, or using in-car devices) from in-vehicle ca
Externí odkaz:
http://arxiv.org/abs/2408.16621
Autor:
Barman, Niyar R, Sharma, Krish, Aziz, Ashhar, Bajpai, Shashwat, Biswas, Shwetangshu, Sharma, Vasu, Jain, Vinija, Chadha, Aman, Sheth, Amit, Das, Amitava
The rapid advancement of text-to-image generation systems, exemplified by models like Stable Diffusion, Midjourney, Imagen, and DALL-E, has heightened concerns about their potential misuse. In response, companies like Meta and Google have intensified
Externí odkaz:
http://arxiv.org/abs/2408.10446
Autor:
Prasad, Renjith, Shyalika, Chathurangi, Zand, Ramtin, Kalach, Fadi El, Venkataramanan, Revathy, Harik, Ramy, Sheth, Amit
Publikováno v:
Predictive Models in Engineering Applications special session (MLPMEA) at International Conference on Machine Learning and Applications (ICMLA) 2024
Anomaly detection in manufacturing pipelines remains a critical challenge, intensified by the complexity and variability of industrial environments. This paper introduces AssemAI, an interpretable image-based anomaly detection system tailored for sma
Externí odkaz:
http://arxiv.org/abs/2408.02181
Generative AI, especially via Large Language Models (LLMs), has transformed content creation across text, images, and music, showcasing capabilities in following instructions through prompting, largely facilitated by instruction tuning. Instruction t
Externí odkaz:
http://arxiv.org/abs/2407.18722